Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing
Machine Learning
2024-01-23 v1
Abstract
In this paper, we show that preprocessing data using a variant of rank transformation called 'Average Rank over an Ensemble of Sub-samples (ARES)' makes clustering algorithms robust to data representation and enable them to detect varying density clusters. Our empirical results, obtained using three most widely used clustering algorithms-namely KMeans, DBSCAN, and DP (Density Peak)-across a wide range of real-world datasets, show that clustering after ARES transformation produces better and more consistent results.
Cite
@article{arxiv.2401.11402,
title = {Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing},
author = {Sunil Aryal and Jonathan R. Wells and Arbind Agrahari Baniya and KC Santosh},
journal= {arXiv preprint arXiv:2401.11402},
year = {2024}
}